pysteps.timeseries¶
Methods and models for time series analysis.
pysteps.timeseries.autoregression¶
Methods related to autoregressive AR(p) models.
adjust_lag2_corrcoef1(gamma_1, gamma_2) |
A simple adjustment of lag-2 temporal autocorrelation coefficient to ensure that the resulting AR(2) process is stationary when the parameters are estimated from the Yule-Walker equations. |
adjust_lag2_corrcoef2(gamma_1, gamma_2) |
A more advanced adjustment of lag-2 temporal autocorrelation coefficient to ensure that the resulting AR(2) process is stationary when the parameters are estimated from the Yule-Walker equations. |
ar_acf(gamma[, n]) |
Compute theoretical autocorrelation function (ACF) from the AR(p) model with lag-l, l=1,2,…,p temporal autocorrelation coefficients. |
estimate_ar_params_yw(gamma) |
Estimate the parameters of an AR(p) model from the Yule-Walker equations using the given set of autocorrelation coefficients. |
iterate_ar_model(X, phi[, EPS]) |
Apply an AR(p) model to a time-series of two-dimensional fields. |
pysteps.timeseries.correlation¶
Methods for computing spatial and temporal correlation of time series of two-dimensional fields.
temporal_autocorrelation(X[, MASK]) |
Compute lag-l autocorrelation coefficients gamma_l, l=1,2,…,n-1, for a time series of n two-dimensional input fields. |